This year’s ScaleUp:AI 2025 conference, hosted by Insight Partners, brought together more than 3,000 virtual and in-person attendees from over 70 countries, including hundreds of enterprise executives and investors. As a returning participant and sponsor, Precisely joined a global conversation on how AI is evolving from experimentation to real-world execution and measurable business impact.
What stood out across every keynote and panel was the pragmatic tone. The conversations were no longer about what AI might do, but how organizations are using it today to deliver results and the leadership, governance, and data foundations required to make it work at scale.
1. The Next Wave: AI Moves from Possibility to Pragmatism
The message was clear. AI is no longer a science experiment. It is now good enough to solve real business problems and drive ROI, even as progress remains uneven. Companies that succeed are those that stay customer obsessed, understand their true differentiation, and scale with discipline.
Jeff Horing of Insight Partners emphasized that data remains the ultimate advantage. Incumbents who own it are positioned to win, but growing restrictions on access are reshaping competition. The new moats of this era are speed, contextual data, and practitioner expertise.
As discussions turned to the rise of agentic AI, systems capable of acting autonomously across workflows, the focus remained on responsible execution. The winners will be those who combine automation with governance and ensure every intelligent system is grounded in trusted data.
2. AI as an Organizational Transformation
Across sessions, leaders agreed that AI is no longer an IT initiative but an organizational transformation.
CEOs and boards are now directly involved in AI strategy. Success depends on leadership that creates the space, resources, and incentives for teams to rethink how work gets done.
A recurring message was to use AI internally before taking it to market. By testing and learning within their own operations, companies build the confidence, fluency, and governance needed for responsible deployment at scale.
3. Trust, Transparency, and the Human in the Loop
Trust is emerging as the cornerstone of sustainable AI.
Whether in finance, healthcare, or customer-facing industries, leaders agreed that accountability must remain human, even as systems grow more autonomous. Human judgment, creativity, and oversight are what make AI trustworthy and what safeguard brand reputation when outcomes carry real consequences.
Tony Fadell, the “father of iPod”, reminded the audience that every intelligent interface still meets a human at the other end. True innovation happens when usability, transparency, and ethics align. Organizations that understand how their models are trained, validated, and governed, and that communicate those practices clearly, will earn lasting trust.
Investors are also gravitating toward companies with differentiated data assets and transparent training pipelines, including those combining open-source foundations with proprietary data and governance.
4. Designing for Context, Not Conversation
AI’s most meaningful progress is happening in context awareness rather than conversation. True intelligence lies in understanding the user’s environment, with devices, sensors, voice, and visuals working together to anticipate intent.
The next generation of AI experiences will be ambient and adaptive, integrated into how we live and work rather than limited to chat-based interactions. Context is becoming the new frontier of intelligence.
5. The Shift from Cloud to Edge
The infrastructure story is evolving rapidly. The future of AI will be hybrid, with smaller, efficient models running locally and connecting to cloud intelligence as needed. This balance of cost, privacy, and performance will make AI faster, more sustainable, and more personal.
Global data center investment this year alone is comparable to the GDP of Singapore, a clear sign of the scale and speed of transformation.
6. Leadership and Learning as Core Differentiators
Ethan Mollick of Wharton reframed AI as co-intelligence, a force that amplifies human creativity and decision-making. Prompting, experimentation, and interpretation are becoming essential leadership skills.
Forward-looking organizations are establishing AI Labs to benchmark ideas, test emerging models, and identify which problems are truly AI-ready. The differentiator now is not model performance but how organizations adapt their people, processes, and data foundations to scale AI responsibly.
Leading Responsibly in the Execution Era
As organizations move from AI experimentation to scaled deployment, one principle stands out: trust in data and in people determines success. The companies that thrive will be those that combine innovation with integrity, ensuring every AI decision is transparent, explainable, and built on reliable data with human accountability at the core.
At Precisely, we see this as the defining moment for the enterprise, where data integrity and responsible AI come together to unlock confident, measurable outcomes. AI has entered its execution era. The opportunity now is to build it on a foundation of trusted, contextual, and governed data.
